SEARCHING FOR DUST AROUND HYPER METAL POOR STARS
Bibliographic record
Abstract
We examine the mid-infrared fluxes and spectral energy distributions for stars with iron abundances [Fe/H] <−5, and other metal-poor stars, to eliminate the possibility that their low metallicities are related to the depletion of elements onto dust grains in the formation of a debris disk. Six out of seven stars examined here show no mid-IR excesses. These non-detections rule out many types of circumstellar disks, e.g., a warm debris disk ( T ⩽ 290 K), or debris disks with inner radii ⩽1 AU, such as those associated with the chemically peculiar post-asymptotic giant branch spectroscopic binaries and RV Tau variables. However, we cannot rule out cooler debris disks, nor those with lower flux ratios to their host stars due to, e.g., a smaller disk mass, a larger inner disk radius, an absence of small grains, or even a multicomponent structure, as often found with the chemically peculiar Lambda Bootis stars. The only exception is HE0107-5240, for which a small mid-IR excess near 10 μm is detected at the 2σ level; if the excess is real and associated with this star, it may indicate the presence of (recent) dust-gas winnowing or a binary system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".